To achieve the automatic and rapid detection and sorting of high-brightness reflection metal cylindrical pots， as well as break through the technical problems of slow speed and low efficiency of metal pot surface defect detection， a bi-directional feature pyramid network （BiFPN） was introduced in this study based on the YOLOX network. In addition， a lightweight feature fusion network model was devised on the basis of the attention mechanism， and the lightweight design of the computing model was realized. Meanwhile， the attention mechanism module was employed to learn the channel and space of feature information， effectively alleviating the semantic gap of multi-scale features and improving the detection precision of the model. Considering the unbalanced distribution of the learning weight of the network for difficult and easy classification samples， the classification loss function regarding the attenuation factor was determined. Comparisons of the feature fusion network， classification loss function， and attention mechanism module position ablation were conducted using the metal pot cylindrical surface defect dataset. The experimental results show that the fusion attention mechanism model can effectively identify six types of defects with different shapes， the average detection precision mAP_0.5 of the test set realized 90.92%， and the detection frame rate was 30.84 FPS. Thus， cylindrical surface defects of metal pots can be identified and located， rapidly as well as with high precision， by using the proposed model.